Multi-stage heating and lithium precipitation prevention coordinated control method for low-temperature fast charging of lithium-ion batteries

Abstract

The present invention discloses a coordinated control method for multi-stage heating and lithium precipitation prevention for low-temperature fast charging of lithium-ion batteries. For low-temperature fast charging of electric vehicles, the coupling relationship between heating power and charging performance is fully considered, achieving coordinated control of the heating and charging processes. Specifically, a battery PTC heating circuit is first designed, and a mathematical model is established for the designed circuit. Taking a certain ternary lithium-ion power battery as an example, a lithium precipitation prevention procedure for lithium-ion batteries is proposed. Then, a multi-stage heating method for the PTC heater is designed and optimized using a PSO-GA hybrid algorithm. Ultimately, the risk of lithium precipitation and system energy consumption are reduced while ensuring that the charging time remains essentially unchanged.

Description

Technical Field

[0001] The present invention relates to the technical field of fast charging of lithium-ion batteries for electric vehicles, and more specifically, to a coordinated control method for multi-stage heating and lithium precipitation prevention for low-temperature fast charging of lithium-ion batteries. Background Art

[0002] The charging performance of pure electric vehicles is significantly affected by ambient temperature. In winter, charging current is lower than in summer, and charging times are longer. In extreme temperatures, average charging power can drop by 15%. Considering that many current charging control methods for low-temperature fast charging, designed to shorten charging times, result in charging currents exceeding the maximum acceptable current for a battery without lithium plating, this significantly increases the risk of lithium plating during charging, significantly impacting battery life and safety. Therefore, fast charging of electric vehicles at low temperatures has become one of the main obstacles to their widespread adoption. Summary of the Invention

[0003] The present invention is provided to solve the above-mentioned problems existing in the prior art. Therefore, a multi-stage heating-anti-lithium precipitation coordinated control method for low-temperature fast charging of lithium-ion batteries is needed to achieve the following two purposes:

[0004] First, a battery PTC heater heating circuit was designed, and a mathematical model of the designed circuit was established. Then, a lithium-ion battery anti-lithium fast charging procedure and a multi-stage heating-anti-lithium fast charging coordinated control method for the thermal management system were proposed.

[0005] Second, fast charging time and system energy consumption are selected as optimization objectives. Based on the PSO-GA hybrid algorithm, the proposed multi-stage heating control strategy of the thermal management system suitable for coordinated control of the heating-charging process is optimized to solve the optimal value of the heating power of the thermal management system in different stages.

[0006] The collaborative control method proposed in the invention can reduce the risk of lithium plating and system energy consumption while ensuring that the charging time is basically consistent.

[0007] The present invention provides a lithium-ion battery low-temperature fast-charging multi-stage heating-anti-lithium precipitation coordinated control method, the method comprising:

[0008] The battery is heated based on a battery heating circuit, the battery heating circuit including an electronic water pump, a PTC heater, a battery liquid cooling plate, a low-temperature water tank, an expansion water tank, a first three-way valve, and a second three-way valve, wherein one end of the battery liquid cooling plate is connected to one end of the first three-way valve by a pipe, and the other two ends of the first three-way valve are connected to the low-temperature water tank and the electronic water pump by pipes, respectively; the other end of the battery liquid cooling plate is connected to one end of the PTC heater by a pipe, and the other end of the PTC heater is connected to one end of the second three-way valve by a pipe, and the other two ends of the second three-way valve are connected to the expansion water tank and the electronic water pump by pipes, respectively;

[0009] A mathematical model of the battery heating circuit is established based on the battery heating circuit to calculate the heating condition of the battery by the PTC heater and determine the charging current according to the temperature.

[0010] Furthermore, the charging current is determined according to the temperature, including:

[0011] Determine the maximum acceptable current of the lithium-ion battery without lithium plating in the entire temperature range. When the temperature is below 0℃, the charging current follows the maximum acceptable current of the battery without lithium plating. When the temperature is above 0℃, charging is carried out according to the set current.

[0012] Furthermore, the method further comprises:

[0013] The steps of performing fast charging multi-stage heating based on the battery heating circuit include:

[0014] Establish a PTC segmented heating model;

[0015] Determine the objective function and constraints in the optimization algorithm;

[0016] The PSO-GA hybrid algorithm is used for optimization.

[0017] Furthermore, a PTC segmented heating model is established, including:

[0018] Divide the operating temperature of the battery into multiple heating intervals;

[0019] When the battery enters the heating range where the operating temperature is greater than 30°C, the PTC heater is controlled to stop working;

[0020] When the battery enters the heating range where the operating temperature is less than or equal to 30°C, the PTC heater heats the battery to P i Constant power operation, where P i represents the constant power of the i-th heating interval, and i represents the number of the set heating interval.

[0021] Furthermore, the multiple heating intervals are T bat ≤-15℃、-15℃ <Tbat ≤-10℃、-10℃ <T bat ≤-5℃、-5℃ <T bat ≤0℃、0℃ <T bat ≤5℃、5℃ <T bat ≤10℃、10℃ <T bat ≤15℃、15℃ <T bat ≤20℃、20℃ <T bat ≤25℃、25℃ <T bat ≤30℃ and T bat >30℃.

[0022] Furthermore, the objective function and constraints in the optimization algorithm are determined, including:

[0023] The fast charging time and system energy consumption are taken as optimization targets, and the optimization targets are normalized. During the optimization process, the limited power of the PTC heater, the change trend of the PTC heater, and the SOC of the battery are selected as constraints of the optimization process.

[0024] Furthermore, the PSO-GA hybrid algorithm is used for optimization, including:

[0025] Get the current temperature of the battery and initialize the particle's velocity v i , particle position x i , the particles are the heating power P of the PTC heater at each stage i ;

[0026] For each particle in the current population, determine the individual optimal value pbest based on the position of each particle and the objective function normalized by the two objectives of charging time and system energy consumption, and solve the Pareto optimal frontier. According to the Pareto optimal frontier, obtain the global optimal value gbest of the population. According to the categories of all particles, calculate the category optimal value nbest corresponding to each particle;

[0027] The position of each particle in the next iteration is calculated using the position of each particle, the individual optimal value pbest of each particle, the global optimal value gbest of the group, and the category optimal value best corresponding to each particle;

[0028] Use the genetic algorithm to update all particles in the population to obtain the population for the next iteration, and iterate repeatedly until the preset termination condition is met;

[0029] The Pareto optimal frontier of this iteration is output, and the particles in the Pareto optimal frontier are used as the optimal PTC heating power P i .

[0030] Furthermore, the position of each particle, the individual optimal value pbest of each particle, the global optimal value gbest of the group, and the category optimal value best corresponding to each particle are used to calculate the position of each particle in the next iteration. The implementation process is as follows:

[0031]

[0032]

[0033] Where: represents the j-dimensional velocity component of particle i when the number of iterations is k+1; c1 and c2 are learning factors representing the population experience and the degree to which the particle is affected by the experience, respectively, and c1>0 and c2>0; r1 and r2 are uniformly distributed random numbers with a distribution range of [0,1]; ω represents the inertia weight, which means the difference between the individual velocity when it changes and the original velocity; and They represent the optimal solution pbest encountered in the particle neighborhood and the optimal solution gbest encountered by the current population respectively.

[0034] Furthermore, the preset termination condition for the iteration is to meet the iteration requirements set by the charging time and the system energy consumption.

[0035] The present invention has at least the following beneficial effects:

[0036] (1) A low-temperature fast charging procedure to prevent lithium precipitation was proposed to reduce the risk of lithium precipitation during fast charging while ensuring that the charging time remains basically unchanged.

[0037] (2) The multi-stage heating method of the PTC heater is adopted, and the PSO-GA hybrid algorithm is used for optimization, which can effectively combine the heating control of the thermal management system with the charging status of the battery under the actual state, so that the battery is heated quickly. Under the premise of ensuring that the fast charging time remains basically unchanged, the energy consumption of the entire system can be reduced, and the charging efficiency can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 shows a structural diagram of a battery heating circuit according to an embodiment of the present invention;

[0039] Figure 2 A schematic diagram showing the maximum acceptable current of a lithium-ion battery without lithium plating over the entire temperature range according to an embodiment of the present invention is shown;

[0040] Figure 3 A flow chart of a multi-stage heating method according to an embodiment of the present invention is shown;

[0041] Figure 4 A schematic diagram showing the division of the operating temperature of a battery according to an embodiment of the present invention is shown;

[0042] Figure 5 A flow chart of a PSO-GA hybrid algorithm according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0043] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments. The embodiments of the present invention are further described in detail below with reference to the accompanying drawings and specific embodiments, but are not intended to limit the present invention. For the various steps described herein, if there is no necessity for a contextual relationship between each other, the order in which they are described as examples herein should not be regarded as limiting, and those skilled in the art should know that they can be adjusted in order as long as the logic between them is not destroyed, resulting in the inability to implement the entire process.

[0044] An embodiment of the present invention provides a coordinated control method for multi-stage heating and lithium precipitation prevention for low-temperature fast charging of lithium-ion batteries. This method, specifically for low-temperature fast charging of electric vehicles, fully considers the coupling relationship between heating power and charging performance, achieving coordinated control of the heating and charging processes. First, a battery PTC heating circuit was designed, and a mathematical model was established for the designed circuit. Using a certain ternary lithium-ion power battery as an example, a lithium precipitation prevention procedure for lithium-ion batteries was proposed. A multi-stage heating method for the PTC heater was then designed and optimized using a PSO-GA hybrid algorithm. Ultimately, the risk of lithium precipitation and system energy consumption were reduced while ensuring that charging time remained essentially unchanged.

[0045] The specific implementation process of this method is as follows:

[0046] First, as attached Figure 1 The present invention designs a battery PTC heating circuit, which includes an electronic water pump, a PTC heater, a battery liquid cooling plate, a low-temperature water tank, an expansion water tank, a first three-way valve and a second three-way valve, wherein one end of the battery liquid cooling plate is connected to one end of the first three-way valve by a pipe, and the other two ends of the first three-way valve are respectively connected to the low-temperature water tank and the electronic water pump by pipes, the other end of the battery liquid cooling plate is connected to one end of the PTC heater by a pipe, and the other end of the PTC heater is connected to one end of the second three-way valve by a pipe, and the other two ends of the second three-way valve are respectively connected to the expansion water tank and the electronic water pump by pipes.

[0047] During the battery heating process, the PTC thermistor acts as a heat source to convert the battery's electrical energy into thermal energy of the coolant. The heated coolant enters the battery's liquid cooling plate and is heated through direct contact with the battery pack. The electronic water pump serves as the power source in this cycle, and the fast charging and heating method described in the present invention is controlled based on this circuit.

[0048] For the above loop, a mathematical model of the heating loop is established, and the specific implementation method is as follows:

[0049] S11: The PTC heater in the battery circuit heats the coolant as follows:

[0050] P PTC η PTC K PTC =m l,PTC C l ×|T l,Pout -T l,Pin |

[0051] Among them, P PTC is the PTC heater power, η PTC is the PTC heater efficiency, K PTC is the heat transfer efficiency from the PTC heater to the coolant, m l,PTC is the mass flow rate of coolant flowing through the PTC heater, T l,Pout 、T l,Pin are the coolant temperatures at the outlet and inlet of the PTC heater, respectively.

[0052] S12: The coolant heated by the PTC heater flows into the cold plate at the bottom of the battery pack to heat the battery pack. The heat exchange rate is calculated by the following formula:

[0053] q p =A pb K pb ΔT pb =m l,p C l ×|T l,Pout -T l,Pin |

[0054] where q p is the heat transfer rate at the bottom cold plate, A pb is the heat transfer area, K pb is the total heat transfer coefficient, ΔT pb is the difference between the average temperature of the coolant in the cold plate and the battery, m l,p is the mass flow rate of coolant at the cold plate, T l,Pout 、T l,Pin are the coolant temperatures at the outlet and inlet of the bottom cold plate, respectively.

[0055] Attachment Figure 2 The maximum charging current that the battery can accept without lithium plating in the whole temperature range. In the lithium plating-free procedure proposed in the present invention, when the battery temperature is below 0°C, the charging current follows the Figure 2 The maximum acceptable current for the battery without lithium plating is: when the temperature is greater than 0℃, the battery charging current follows the battery's original charging procedure. In summary, the battery's lithium plating-free charging procedure is established.

[0056] In this embodiment, a fast charging multi-stage heating method is provided to achieve coordinated control of battery fast charging and heating. The multi-stage heating method is implemented as shown in the attached flow chart. Figure 3 As shown, the specific steps include:

[0057] S1: Establish PTC segmented heating model:

[0058] As attached Figure 4 , first divide the operating temperature of the battery into T bat ≤-15, -15 <T bat ≤-10, -10 <T bat ≤-5, -5 <T bat ≤0, 0 <T bat ≤5, 5 <T bat ≤10, 10 <T bat ≤15, 15 <T bat ≤20, 20 <T bat ≤25, 25 <T bat ≤30 and T bat >30 (unit ℃) in these eleven temperature ranges, in order to prevent the battery from overheating, when the battery temperature is greater than 30 ℃, the PTC heater is controlled to stop working. When entering other stages, the PTC heater heats with P i (i=1, 2, ..., 10) constant power operation.

[0059] S2: Determine the objective function and constraints in the optimization algorithm. The specific steps are as follows:

[0060] S21: Establish the objective function as follows:

[0061] The proposed multi-stage heating-anti-lithium fast charging method is optimized based on the PSO-GA hybrid algorithm. First, the fast charging time and system energy consumption are selected as optimization targets. Since the orders of magnitude and units of the two targets are different, they are normalized as shown below:

[0062]

[0063] Where J is the normalized value of the target, f is the target value, and f max and f min are the maximum and minimum values ​​of the target, respectively.

[0064] During the optimization process, we want to obtain the shortest charging time and heating energy consumption, so the optimization objective function is constructed as follows:

[0065] f=min[α×J t +(1-α)×JE ]

[0066] Where α is the weight coefficient, J t and J E are the normalized charging time and heating energy consumption of the thermal management system, respectively.

[0067] S22: During the optimization process, the constraints are as follows:

[0068] The designed optimization constraints are the PTC heater power, heater power variation, and SOC. The heater power should be less than the maximum power limit. To prevent battery overheating during fast charging, the PTC heater power must be controlled to decrease as the battery temperature increases. The maximum SOC of the battery during fast charging is 80%, as shown below:

[0069]

[0070] S3: After completing the selection of the objective function and constraints, the PSO-GA hybrid algorithm is used for optimization. During the optimization, the present invention introduces the Pareto theory. For multi-objective optimization, the influence between the objectives is large. Improving the performance of one objective may cause a significant change in the performance of other objectives. Therefore, the global optimal solution is often not the only solution, so the Pareto optimal solution is used for solution. The optimization process is shown in the attached figure. Figure 5 , the specific steps are as follows:

[0071] S31: Create a population according to the preset initial parameters and initialize the position of each particle in the population. In the present invention, the current temperature of the battery is first obtained, and then the velocity v of the particle is initialized. i , particle position x i .

[0072] Specifically, the particles are the heating power P of the PTC heater at each stage. i (i=1,2,…10). Set the maximum number of iterations T to 100, the population size N to 100, and set the initial values ​​of the particle individual optimal value pbest, the group global optimal value gbest, and the category optimal value nbest to 10. 5 .

[0073] S32: For each particle in the current population, determine the optimal value pbest of the normalized objective function f according to the position of each particle, the charging time, and the system energy consumption, and solve the Pareto optimal frontier. According to the Pareto optimal frontier, obtain the global optimal value gbest of the population. According to the categories of all particles, calculate the category optimal value nbest corresponding to each particle.

[0074] Specifically, the two objective function values ​​of each particle are compared with the two objective function values ​​of the corresponding individual optimal value pbest in the previous iteration. If the two objective function values ​​of the particle are better than the corresponding individual optimal value pbest in the previous iteration, that is, the particle dominates the corresponding individual optimal value pbest in the previous iteration, the current position of the particle is used as the individual optimal value pbest corresponding to this iteration. If the particle does not dominate the corresponding individual optimal value pbest in the previous iteration, the current position of the particle or the individual optimal value pbest corresponding to the previous iteration is randomly selected as the individual optimal value pbest corresponding to this iteration.

[0075] S33: Calculate the position of each particle in the next iteration using the position of each particle, the individual optimal value pbest of each particle, the global optimal value gbest of the group, and the optimal value nbest of the category corresponding to each particle. The specific implementation process is as follows:

[0076]

[0077]

[0078] Where: represents the j-dimensional velocity component of particle i when the number of iterations is k+1; c1 and c2 are learning factors representing the population experience and the degree to which the particle is affected by the experience, respectively, and c1>0 and c2>0; r1 and r2 are uniformly distributed random numbers with a distribution range of [0,1]; ω represents the inertia weight, which means the difference between the individual velocity when it changes and the original velocity. and The above process ensures the diversity of the population, which is consistent with the situation in which the PTC heater is heated in multiple intervals in the present invention, making the calculation results more realistic.

[0079] S34: Using the genetic algorithm to update all particles in the population to obtain a population for the next iteration, and returning to step S32 until a preset termination condition is met;

[0080] In the present invention, step S34 specifically includes the following steps:

[0081] (1) Initial crossover score P c is 0.8, the mutation probability P m is 0.1.

[0082] (2) Select the parent individuals in the population and determine the mother individuals according to the optimal value nbest of each category, and the crossover probability P cPerform a crossover operation on the parent individual and the mother individual to obtain the first new particle, according to the crossover probability P m performing a mutation operation on the first new particle to obtain a second new particle;

[0083] (3) Using the elite retention method to screen and retain the target particles in the population, the target particles and the second new particles are used to form a population for the next iteration, the number of target particles retained in the population is N / 3 rounded up, and the remaining number of second new particles is selected to replace other particles that are not retained.

[0084] (4) When the iteration conditions are met, the iteration ends.

[0085] S4: Output the Pareto optimal frontier of the last iteration, and the particles in the Pareto optimal frontier are used as the optimal PTC heating power P in each interval. i .

[0086] Furthermore, although exemplary embodiments have been described herein, the scope includes any and all embodiments based on the present invention having equivalent elements, modifications, omissions, combinations (e.g., schemes where various embodiments intersect), adaptations, or changes. The elements in the claims are to be interpreted broadly based on the language employed in the claims and are not limited to the examples described in this specification or during the prosecution of this application, which examples are to be interpreted as non-exclusive. Therefore, this specification and examples are intended to be considered as examples only, with the true scope and spirit being indicated by the following claims and the full scope of their equivalents.

[0087] The above description is intended to be illustrative rather than restrictive. For example, the above examples (or one or more of their solutions) can be used in combination with each other. For example, those of ordinary skill in the art may use other embodiments when reading the above description. In addition, in the above-mentioned specific embodiments, various features can be grouped together to simplify the present invention. This should not be interpreted as an intention that a feature of an invention that is not claimed for protection is necessary for any claim. On the contrary, the subject matter of the present invention may be less than all the features of the embodiments of a particular invention. Thus, the following claims are incorporated into the specific embodiments as examples or embodiments, wherein each claim is independently a separate embodiment, and it is considered that these embodiments can be combined with each other in various combinations or arrangements. The scope of the present invention should be determined with reference to the appended claims and the full scope of equivalents to which these claims are entitled.

Claims

1. A lithium-ion battery low-temperature fast charging multi-stage heating-anti-lithium precipitation coordinated control method, characterized in that: The method comprises: The battery is heated based on a battery heating circuit, the battery heating circuit including an electronic water pump, a PTC heater, a battery liquid cooling plate, a low-temperature water tank, an expansion water tank, a first three-way valve, and a second three-way valve, wherein one end of the battery liquid cooling plate is connected to one end of the first three-way valve by a pipe, and the other two ends of the first three-way valve are connected to the low-temperature water tank and the electronic water pump by pipes, respectively; the other end of the battery liquid cooling plate is connected to one end of the PTC heater by a pipe, and the other end of the PTC heater is connected to one end of the second three-way valve by a pipe, and the other two ends of the second three-way valve are connected to the expansion water tank and the electronic water pump by pipes, respectively; Establishing a battery heating circuit mathematical model based on the battery heating circuit to calculate the heating effect of the PTC heater on the battery and determine the charging current according to the temperature; Determine the charge current based on temperature, including: Determine the maximum acceptable current of the lithium-ion battery without lithium plating in the full temperature range. When the temperature is below 0°C, the charging current follows the maximum acceptable current without lithium plating. When the temperature is above 0°C, charging is carried out according to the set current. The method further comprises: The steps of performing fast charging multi-stage heating based on the battery heating circuit include: Establish a PTC segmented heating model; Determine the objective function and constraints in the optimization algorithm; Optimization is performed using the PSO-GA hybrid algorithm; Determine the objective function and constraints in the optimization algorithm, including: Taking fast charging time and system energy consumption as optimization targets, the optimization targets are normalized. During the optimization process, the limited power of the PTC heater, the change trend of the PTC heater, and the battery SOC are selected as constraints of the optimization process; Optimization using the PSO-GA hybrid algorithm includes: Get the current temperature of the battery and initialize the particle's velocity v i , particle position x i , the particles are the heating power P of the PTC heater at each stage i ; For each particle in the current population, the individual optimal value pbest is determined based on the objective function normalized by the position of each particle and the charging time and system energy consumption, and the Pareto optimal frontier is solved. The global optimal value gbest of the population is obtained based on the Pareto optimal frontier. The category optimal value nbest corresponding to each particle is calculated based on the categories of all particles. The position of each particle in the next iteration is calculated using the position of each particle, the individual optimal value pbest of each particle, the global optimal value gbest of the group, and the category optimal value best corresponding to each particle; Use the genetic algorithm to update all particles in the population to obtain the population for the next iteration, and iterate repeatedly until the preset termination condition is met; The Pareto optimal frontier of this iteration is output, and the particles in the Pareto optimal frontier are used as the optimal PTC heating power P i .

2. The method according to claim 1, characterized in that Establish a PTC segmented heating model, including: Divide the operating temperature of the battery into multiple heating intervals; When the battery enters the heating range where the operating temperature is greater than 30°C, the PTC heater is controlled to stop working; When the battery enters the heating range where the operating temperature is less than or equal to 30°C, the PTC heater heats the battery to P i Constant power operation, where P i It represents the constant power of the i-th heating interval, and i represents the number of the set heating interval.

3. The method according to claim 2, characterized in that The multiple heating intervals are T bat ≤-15℃、-15℃ <T bat ≤-10℃、-10℃ <T bat ≤-5℃、-5℃ <T bat ≤0℃、0℃ <T bat ≤5℃、5℃ <T bat ≤10℃、10℃ <T bat ≤15℃、15℃ <T bat ≤20℃、20℃ <T bat ≤25℃、25℃ <T bat ≤30℃ and T bat >30℃.

4. The method according to claim 1, wherein The process of calculating the position of each particle in the next iteration using the position of each particle, the individual optimal value pbest of each particle, the global optimal value gbest of the group, and the category optimal value best corresponding to each particle is as follows: Where: represents the j-dimensional velocity component of particle i when the number of iterations is k+1; c1 and c2 are learning factors representing the population experience and the degree to which the particle is affected by the experience, respectively, and c1>0 and c2>0; r1 and r2 are uniformly distributed random numbers with a distribution range of [0,1]; ω represents the inertia weight, which means the difference between the individual velocity when it changes and the original velocity; and They represent the optimal solution pbest encountered in the particle neighborhood and the optimal solution gbest encountered by the current population respectively.

5. The method according to claim 1, wherein The preset termination condition for the iteration is to meet the iteration requirements set by the charging time and system energy consumption.

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